Overview

Dataset statistics

 Before imputationAfter imputation
Number of variables1614
Number of observations86938693
Missing cells23230
Missing cells (%)1.7%0.0%
Duplicate rows0504
Duplicate rows (%)0.0%5.8%
Total size in memory1.1 MiB832.1 KiB
Average record size in memory128.0 B98.0 B

Variable types

 Before imputationAfter imputation
Categorical54
Boolean22
Numeric98

Alerts

Before imputationAfter imputation
VIP is highly imbalanced (84.0%) VIP is highly imbalanced (84.3%) Imbalance
HomePlanet has 201 (2.3%) missing values Alert not present in this datasetMissing
CryoSleep has 217 (2.5%) missing values Alert not present in this datasetMissing
Destination has 182 (2.1%) missing values Alert not present in this datasetMissing
Age has 179 (2.1%) missing values Alert not present in this datasetMissing
VIP has 203 (2.3%) missing values Alert not present in this datasetMissing
RoomService has 181 (2.1%) missing values Alert not present in this datasetMissing
FoodCourt has 183 (2.1%) missing values Alert not present in this datasetMissing
ShoppingMall has 208 (2.4%) missing values Alert not present in this datasetMissing
Spa has 183 (2.1%) missing values Alert not present in this datasetMissing
VRDeck has 188 (2.2%) missing values Alert not present in this datasetMissing
Cabin_deck has 199 (2.3%) missing values Alert not present in this datasetMissing
Cabin_side has 199 (2.3%) missing values Alert not present in this datasetMissing
Age has 178 (2.0%) zeros Age has 178 (2.0%) zeros Zeros
RoomService has 5577 (64.2%) zeros RoomService has 5651 (65.0%) zeros Zeros
FoodCourt has 5456 (62.8%) zeros FoodCourt has 5533 (63.6%) zeros Zeros
ShoppingMall has 5587 (64.3%) zeros ShoppingMall has 5692 (65.5%) zeros Zeros
Spa has 5324 (61.2%) zeros Spa has 5393 (62.0%) zeros Zeros
VRDeck has 5495 (63.2%) zeros VRDeck has 5576 (64.1%) zeros Zeros
Alert not present in this dataset Dataset has 504 (5.8%) duplicate rowsDuplicates

Reproduction

 Before imputationAfter imputation
Analysis started2024-04-23 18:25:49.8879402024-04-23 18:26:06.761971
Analysis finished2024-04-23 18:26:06.7491862024-04-23 18:26:19.481524
Duration16.86 seconds12.72 seconds
Software versionydata-profiling vv4.7.0ydata-profiling vv4.7.0
Download configurationconfig.jsonconfig.json

Variables

HomePlanet
Categorical

 Before imputationAfter imputation
Distinct33
Distinct (%)< 0.1%< 0.1%
Missing2010
Missing (%)2.3%0.0%
Memory size68.0 KiB68.0 KiB
Earth
4602 
Europa
2131 
Mars
1759 
Earth
4709 
Europa
2175 
Mars
1809 

Length

 Before imputationAfter imputation
Max length66
Median length55
Mean length5.04380595.0421028
Min length44

Characters and Unicode

 Before imputationAfter imputation
Total characters4283243831
Distinct characters1010
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Before imputationAfter imputation
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Before imputationAfter imputation
1st rowEuropaEuropa
2nd rowEarthEarth
3rd rowEuropaEuropa
4th rowEuropaEuropa
5th rowEarthEarth

Common Values

ValueCountFrequency (%)
Earth 4602
52.9%
Europa 2131
24.5%
Mars 1759
 
20.2%
(Missing) 201
 
2.3%
ValueCountFrequency (%)
Earth 4709
54.2%
Europa 2175
25.0%
Mars 1809
 
20.8%

Length

2024-04-23T20:26:19.716146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Before imputation

2024-04-23T20:26:19.947001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:20.115836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
earth 4602
54.2%
europa 2131
25.1%
mars 1759
 
20.7%
ValueCountFrequency (%)
earth 4709
54.2%
europa 2175
25.0%
mars 1809
 
20.8%

Most occurring characters

ValueCountFrequency (%)
a 8492
19.8%
r 8492
19.8%
E 6733
15.7%
t 4602
10.7%
h 4602
10.7%
u 2131
 
5.0%
o 2131
 
5.0%
p 2131
 
5.0%
M 1759
 
4.1%
s 1759
 
4.1%
ValueCountFrequency (%)
a 8693
19.8%
r 8693
19.8%
E 6884
15.7%
t 4709
10.7%
h 4709
10.7%
u 2175
 
5.0%
o 2175
 
5.0%
p 2175
 
5.0%
M 1809
 
4.1%
s 1809
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42832
100.0%
ValueCountFrequency (%)
(unknown) 43831
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8492
19.8%
r 8492
19.8%
E 6733
15.7%
t 4602
10.7%
h 4602
10.7%
u 2131
 
5.0%
o 2131
 
5.0%
p 2131
 
5.0%
M 1759
 
4.1%
s 1759
 
4.1%
ValueCountFrequency (%)
a 8693
19.8%
r 8693
19.8%
E 6884
15.7%
t 4709
10.7%
h 4709
10.7%
u 2175
 
5.0%
o 2175
 
5.0%
p 2175
 
5.0%
M 1809
 
4.1%
s 1809
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42832
100.0%
ValueCountFrequency (%)
(unknown) 43831
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8492
19.8%
r 8492
19.8%
E 6733
15.7%
t 4602
10.7%
h 4602
10.7%
u 2131
 
5.0%
o 2131
 
5.0%
p 2131
 
5.0%
M 1759
 
4.1%
s 1759
 
4.1%
ValueCountFrequency (%)
a 8693
19.8%
r 8693
19.8%
E 6884
15.7%
t 4709
10.7%
h 4709
10.7%
u 2175
 
5.0%
o 2175
 
5.0%
p 2175
 
5.0%
M 1809
 
4.1%
s 1809
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42832
100.0%
ValueCountFrequency (%)
(unknown) 43831
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8492
19.8%
r 8492
19.8%
E 6733
15.7%
t 4602
10.7%
h 4602
10.7%
u 2131
 
5.0%
o 2131
 
5.0%
p 2131
 
5.0%
M 1759
 
4.1%
s 1759
 
4.1%
ValueCountFrequency (%)
a 8693
19.8%
r 8693
19.8%
E 6884
15.7%
t 4709
10.7%
h 4709
10.7%
u 2175
 
5.0%
o 2175
 
5.0%
p 2175
 
5.0%
M 1809
 
4.1%
s 1809
 
4.1%

CryoSleep
Boolean

 Before imputationAfter imputation
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing2170
Missing (%)2.5%0.0%
Memory size68.0 KiB8.6 KiB
False
5439 
True
3037 
(Missing)
 
217
False
5571 
True
3122 
ValueCountFrequency (%)
False 5439
62.6%
True 3037
34.9%
(Missing) 217
 
2.5%
ValueCountFrequency (%)
False 5571
64.1%
True 3122
35.9%

Before imputation

2024-04-23T20:26:20.275455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:20.444355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Destination
Categorical

 Before imputationAfter imputation
Distinct33
Distinct (%)< 0.1%< 0.1%
Missing1820
Missing (%)2.1%0.0%
Memory size68.0 KiB68.0 KiB
TRAPPIST-1e
5915 
55 Cancri e
1800 
PSO J318.5-22
796 
TRAPPIST-1e
6086 
55 Cancri e
1811 
PSO J318.5-22
796 

Length

 Before imputationAfter imputation
Max length1313
Median length1111
Mean length11.18705211.183136
Min length1111

Characters and Unicode

 Before imputationAfter imputation
Total characters9521397215
Distinct characters2323
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Before imputationAfter imputation
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Before imputationAfter imputation
1st rowTRAPPIST-1eTRAPPIST-1e
2nd rowTRAPPIST-1eTRAPPIST-1e
3rd rowTRAPPIST-1eTRAPPIST-1e
4th rowTRAPPIST-1eTRAPPIST-1e
5th rowTRAPPIST-1eTRAPPIST-1e

Common Values

ValueCountFrequency (%)
TRAPPIST-1e 5915
68.0%
55 Cancri e 1800
 
20.7%
PSO J318.5-22 796
 
9.2%
(Missing) 182
 
2.1%
ValueCountFrequency (%)
TRAPPIST-1e 6086
70.0%
55 Cancri e 1811
 
20.8%
PSO J318.5-22 796
 
9.2%

Length

2024-04-23T20:26:20.639140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Before imputation

2024-04-23T20:26:20.852404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:21.044303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
trappist-1e 5915
45.8%
55 1800
 
13.9%
cancri 1800
 
13.9%
e 1800
 
13.9%
pso 796
 
6.2%
j318.5-22 796
 
6.2%
ValueCountFrequency (%)
trappist-1e 6086
46.4%
55 1811
 
13.8%
cancri 1811
 
13.8%
e 1811
 
13.8%
pso 796
 
6.1%
j318.5-22 796
 
6.1%

Most occurring characters

ValueCountFrequency (%)
P 12626
13.3%
T 11830
12.4%
e 7715
 
8.1%
S 6711
 
7.0%
- 6711
 
7.0%
1 6711
 
7.0%
A 5915
 
6.2%
I 5915
 
6.2%
R 5915
 
6.2%
5 4396
 
4.6%
Other values (13) 20768
21.8%
ValueCountFrequency (%)
P 12968
13.3%
T 12172
12.5%
e 7897
 
8.1%
S 6882
 
7.1%
- 6882
 
7.1%
1 6882
 
7.1%
A 6086
 
6.3%
I 6086
 
6.3%
R 6086
 
6.3%
5 4418
 
4.5%
Other values (13) 20856
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 95213
100.0%
ValueCountFrequency (%)
(unknown) 97215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 12626
13.3%
T 11830
12.4%
e 7715
 
8.1%
S 6711
 
7.0%
- 6711
 
7.0%
1 6711
 
7.0%
A 5915
 
6.2%
I 5915
 
6.2%
R 5915
 
6.2%
5 4396
 
4.6%
Other values (13) 20768
21.8%
ValueCountFrequency (%)
P 12968
13.3%
T 12172
12.5%
e 7897
 
8.1%
S 6882
 
7.1%
- 6882
 
7.1%
1 6882
 
7.1%
A 6086
 
6.3%
I 6086
 
6.3%
R 6086
 
6.3%
5 4418
 
4.5%
Other values (13) 20856
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 95213
100.0%
ValueCountFrequency (%)
(unknown) 97215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 12626
13.3%
T 11830
12.4%
e 7715
 
8.1%
S 6711
 
7.0%
- 6711
 
7.0%
1 6711
 
7.0%
A 5915
 
6.2%
I 5915
 
6.2%
R 5915
 
6.2%
5 4396
 
4.6%
Other values (13) 20768
21.8%
ValueCountFrequency (%)
P 12968
13.3%
T 12172
12.5%
e 7897
 
8.1%
S 6882
 
7.1%
- 6882
 
7.1%
1 6882
 
7.1%
A 6086
 
6.3%
I 6086
 
6.3%
R 6086
 
6.3%
5 4418
 
4.5%
Other values (13) 20856
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 95213
100.0%
ValueCountFrequency (%)
(unknown) 97215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 12626
13.3%
T 11830
12.4%
e 7715
 
8.1%
S 6711
 
7.0%
- 6711
 
7.0%
1 6711
 
7.0%
A 5915
 
6.2%
I 5915
 
6.2%
R 5915
 
6.2%
5 4396
 
4.6%
Other values (13) 20768
21.8%
ValueCountFrequency (%)
P 12968
13.3%
T 12172
12.5%
e 7897
 
8.1%
S 6882
 
7.1%
- 6882
 
7.1%
1 6882
 
7.1%
A 6086
 
6.3%
I 6086
 
6.3%
R 6086
 
6.3%
5 4418
 
4.5%
Other values (13) 20856
21.5%

Age
Real number (ℝ)

 Before imputationAfter imputation
Distinct80204
Distinct (%)0.9%2.3%
Missing1790
Missing (%)2.1%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean28.8279328.83621
 Before imputationAfter imputation
Minimum00
Maximum7979
Zeros178178
Zeros (%)2.0%2.0%
Negative00
Negative (%)0.0%0.0%
Memory size68.0 KiB68.0 KiB
2024-04-23T20:26:21.285126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 Before imputationAfter imputation
Minimum00
5-th percentile44
Q11920
median2727
Q33837
95-th percentile5655
Maximum7979
Range7979
Interquartile range (IQR)1917

Descriptive statistics

 Before imputationAfter imputation
Standard deviation14.48902114.361932
Coefficient of variation (CV)0.502603590.49805199
Kurtosis0.101932920.14809039
Mean28.8279328.83621
Median Absolute Deviation (MAD)99
Skewness0.419096580.41898548
Sum245441250673.17
Variance209.93174206.26508
MonotonicityNot monotonicNot monotonic
2024-04-23T20:26:21.566751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 324
 
3.7%
18 320
 
3.7%
21 311
 
3.6%
19 293
 
3.4%
23 292
 
3.4%
22 291
 
3.3%
20 277
 
3.2%
26 268
 
3.1%
28 267
 
3.1%
27 259
 
3.0%
Other values (70) 5612
64.6%
ValueCountFrequency (%)
24 324
 
3.7%
18 320
 
3.7%
21 311
 
3.6%
19 293
 
3.4%
23 292
 
3.4%
22 291
 
3.3%
20 277
 
3.2%
26 268
 
3.1%
28 267
 
3.1%
27 259
 
3.0%
Other values (194) 5791
66.6%
ValueCountFrequency (%)
0 178
2.0%
1 67
 
0.8%
2 75
0.9%
3 75
0.9%
4 71
 
0.8%
5 33
 
0.4%
6 40
 
0.5%
7 52
 
0.6%
8 46
 
0.5%
9 42
 
0.5%
ValueCountFrequency (%)
0 178
2.0%
1 67
 
0.8%
2 75
0.9%
3 75
0.9%
4 71
 
0.8%
5 33
 
0.4%
6 40
 
0.5%
7 52
 
0.6%
8 46
 
0.5%
8.451165929 1
 
< 0.1%
ValueCountFrequency (%)
0 178
2.0%
1 67
 
0.8%
2 75
0.9%
3 75
0.9%
4 71
 
0.8%
5 33
 
0.4%
6 40
 
0.5%
7 52
 
0.6%
8 46
 
0.5%
8.451165929 1
 
< 0.1%
ValueCountFrequency (%)
0 178
2.0%
1 67
 
0.8%
2 75
0.9%
3 75
0.9%
4 71
 
0.8%
5 33
 
0.4%
6 40
 
0.5%
7 52
 
0.6%
8 46
 
0.5%
9 42
 
0.5%

VIP
Boolean

 Before imputationAfter imputation
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing2030
Missing (%)2.3%0.0%
Memory size68.0 KiB8.6 KiB
False
8291 
True
 
199
(Missing)
 
203
False
8495 
True
 
198
ValueCountFrequency (%)
False 8291
95.4%
True 199
 
2.3%
(Missing) 203
 
2.3%
ValueCountFrequency (%)
False 8495
97.7%
True 198
 
2.3%

Before imputation

2024-04-23T20:26:21.765683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:21.938051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

RoomService
Real number (ℝ)

 Before imputationAfter imputation
Distinct12731380
Distinct (%)15.0%15.9%
Missing1810
Missing (%)2.1%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean224.68762224.53839
 Before imputationAfter imputation
Minimum0-232.32033
Maximum1432714327
Zeros55775651
Zeros (%)64.2%65.0%
Negative010
Negative (%)0.0%0.1%
Memory size68.0 KiB68.0 KiB
2024-04-23T20:26:22.202499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 Before imputationAfter imputation
Minimum0-232.32033
5-th percentile00
Q100
median00
Q34756
95-th percentile1274.251267.4
Maximum1432714327
Range1432714559.32
Interquartile range (IQR)4756

Descriptive statistics

 Before imputationAfter imputation
Standard deviation666.71766661.83779
Coefficient of variation (CV)2.96730932.9475485
Kurtosis65.27380265.865938
Mean224.68762224.53839
Median Absolute Deviation (MAD)00
Skewness6.33301416.3474445
Sum19125411951912.2
Variance444512.44438029.26
MonotonicityNot monotonicNot monotonic
2024-04-23T20:26:22.494941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5577
64.2%
1 117
 
1.3%
2 79
 
0.9%
3 61
 
0.7%
4 47
 
0.5%
5 28
 
0.3%
9 25
 
0.3%
8 24
 
0.3%
6 24
 
0.3%
14 21
 
0.2%
Other values (1263) 2509
28.9%
(Missing) 181
 
2.1%
ValueCountFrequency (%)
0 5651
65.0%
1 117
 
1.3%
2 79
 
0.9%
3 61
 
0.7%
4 47
 
0.5%
5 28
 
0.3%
9 25
 
0.3%
8 24
 
0.3%
6 24
 
0.3%
14 21
 
0.2%
Other values (1370) 2616
30.1%
ValueCountFrequency (%)
0 5577
64.2%
1 117
 
1.3%
2 79
 
0.9%
3 61
 
0.7%
4 47
 
0.5%
5 28
 
0.3%
6 24
 
0.3%
7 17
 
0.2%
8 24
 
0.3%
9 25
 
0.3%
ValueCountFrequency (%)
-232.3203344 1
< 0.1%
-95.69703395 1
< 0.1%
-85.73948362 1
< 0.1%
-69.5474572 1
< 0.1%
-43.18417992 1
< 0.1%
-27.72422618 1
< 0.1%
-17.40291265 1
< 0.1%
-15.59733242 1
< 0.1%
-6.729029689 1
< 0.1%
-1.542926493 1
< 0.1%
ValueCountFrequency (%)
-232.3203344 1
< 0.1%
-95.69703395 1
< 0.1%
-85.73948362 1
< 0.1%
-69.5474572 1
< 0.1%
-43.18417992 1
< 0.1%
-27.72422618 1
< 0.1%
-17.40291265 1
< 0.1%
-15.59733242 1
< 0.1%
-6.729029689 1
< 0.1%
-1.542926493 1
< 0.1%
ValueCountFrequency (%)
0 5577
64.2%
1 117
 
1.3%
2 79
 
0.9%
3 61
 
0.7%
4 47
 
0.5%
5 28
 
0.3%
6 24
 
0.3%
7 17
 
0.2%
8 24
 
0.3%
9 25
 
0.3%

FoodCourt
Real number (ℝ)

 Before imputationAfter imputation
Distinct15071613
Distinct (%)17.7%18.6%
Missing1830
Missing (%)2.1%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean458.0772456.12828
 Before imputationAfter imputation
Minimum0-330.2559
Maximum2981329813
Zeros54565533
Zeros (%)62.8%63.6%
Negative09
Negative (%)0.0%0.1%
Memory size68.0 KiB68.0 KiB
2024-04-23T20:26:22.801466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 Before imputationAfter imputation
Minimum0-330.2559
5-th percentile00
Q100
median00
Q37686
95-th percentile2748.52749.4679
Maximum2981329813
Range2981330143.256
Interquartile range (IQR)7686

Descriptive statistics

 Before imputationAfter imputation
Standard deviation1611.48921600.4771
Coefficient of variation (CV)3.51794253.5088312
Kurtosis73.3072373.861567
Mean458.0772456.12828
Median Absolute Deviation (MAD)00
Skewness7.10222797.1170725
Sum38982373965123.2
Variance2596897.62561527
MonotonicityNot monotonicNot monotonic
2024-04-23T20:26:23.081844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5456
62.8%
1 116
 
1.3%
2 75
 
0.9%
3 53
 
0.6%
4 53
 
0.6%
5 33
 
0.4%
6 31
 
0.4%
9 28
 
0.3%
7 27
 
0.3%
10 27
 
0.3%
Other values (1497) 2611
30.0%
(Missing) 183
 
2.1%
ValueCountFrequency (%)
0 5533
63.6%
1 116
 
1.3%
2 75
 
0.9%
3 53
 
0.6%
4 53
 
0.6%
5 33
 
0.4%
6 31
 
0.4%
9 28
 
0.3%
7 27
 
0.3%
10 27
 
0.3%
Other values (1603) 2717
31.3%
ValueCountFrequency (%)
0 5456
62.8%
1 116
 
1.3%
2 75
 
0.9%
3 53
 
0.6%
4 53
 
0.6%
5 33
 
0.4%
6 31
 
0.4%
7 27
 
0.3%
8 20
 
0.2%
9 28
 
0.3%
ValueCountFrequency (%)
-330.2559002 1
 
< 0.1%
-252.2289842 1
 
< 0.1%
-198.7968009 1
 
< 0.1%
-160.326611 1
 
< 0.1%
-84.50866249 1
 
< 0.1%
-81.76534395 1
 
< 0.1%
-30.4882525 1
 
< 0.1%
-14.52259282 1
 
< 0.1%
-8.169382772 1
 
< 0.1%
0 5533
63.6%
ValueCountFrequency (%)
-330.2559002 1
 
< 0.1%
-252.2289842 1
 
< 0.1%
-198.7968009 1
 
< 0.1%
-160.326611 1
 
< 0.1%
-84.50866249 1
 
< 0.1%
-81.76534395 1
 
< 0.1%
-30.4882525 1
 
< 0.1%
-14.52259282 1
 
< 0.1%
-8.169382772 1
 
< 0.1%
0 5533
63.6%
ValueCountFrequency (%)
0 5456
62.8%
1 116
 
1.3%
2 75
 
0.9%
3 53
 
0.6%
4 53
 
0.6%
5 33
 
0.4%
6 31
 
0.4%
7 27
 
0.3%
8 20
 
0.2%
9 28
 
0.3%

ShoppingMall
Real number (ℝ)

 Before imputationAfter imputation
Distinct11151218
Distinct (%)13.1%14.0%
Missing2080
Missing (%)2.4%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean173.72917173.10661
 Before imputationAfter imputation
Minimum0-162.15064
Maximum2349223492
Zeros55875692
Zeros (%)64.3%65.5%
Negative010
Negative (%)0.0%0.1%
Memory size68.0 KiB68.0 KiB
2024-04-23T20:26:23.402760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 Before imputationAfter imputation
Minimum0-162.15064
5-th percentile00
Q100
median00
Q32731
95-th percentile927.8926
Maximum2349223492
Range2349223654.151
Interquartile range (IQR)2731

Descriptive statistics

 Before imputationAfter imputation
Standard deviation604.69646599.20486
Coefficient of variation (CV)3.48068473.4614788
Kurtosis328.87091333.04009
Mean173.72917173.10661
Median Absolute Deviation (MAD)00
Skewness12.62756212.67925
Sum14740921504815.8
Variance365657.81359046.46
MonotonicityNot monotonicNot monotonic
2024-04-23T20:26:23.734042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5587
64.3%
1 153
 
1.8%
2 80
 
0.9%
3 59
 
0.7%
4 45
 
0.5%
5 38
 
0.4%
7 36
 
0.4%
6 34
 
0.4%
13 29
 
0.3%
8 28
 
0.3%
Other values (1105) 2396
27.6%
(Missing) 208
 
2.4%
ValueCountFrequency (%)
0 5692
65.5%
1 153
 
1.8%
2 80
 
0.9%
3 59
 
0.7%
4 45
 
0.5%
5 38
 
0.4%
7 36
 
0.4%
6 34
 
0.4%
13 29
 
0.3%
9 28
 
0.3%
Other values (1208) 2499
28.7%
ValueCountFrequency (%)
0 5587
64.3%
1 153
 
1.8%
2 80
 
0.9%
3 59
 
0.7%
4 45
 
0.5%
5 38
 
0.4%
6 34
 
0.4%
7 36
 
0.4%
8 28
 
0.3%
9 28
 
0.3%
ValueCountFrequency (%)
-162.1506372 1
< 0.1%
-137.3657573 1
< 0.1%
-114.561574 1
< 0.1%
-113.5403653 1
< 0.1%
-64.68670373 1
< 0.1%
-55.26010686 1
< 0.1%
-54.86229795 1
< 0.1%
-44.01136393 1
< 0.1%
-29.99700539 1
< 0.1%
-22.36626173 1
< 0.1%
ValueCountFrequency (%)
-162.1506372 1
< 0.1%
-137.3657573 1
< 0.1%
-114.561574 1
< 0.1%
-113.5403653 1
< 0.1%
-64.68670373 1
< 0.1%
-55.26010686 1
< 0.1%
-54.86229795 1
< 0.1%
-44.01136393 1
< 0.1%
-29.99700539 1
< 0.1%
-22.36626173 1
< 0.1%
ValueCountFrequency (%)
0 5587
64.3%
1 153
 
1.8%
2 80
 
0.9%
3 59
 
0.7%
4 45
 
0.5%
5 38
 
0.4%
6 34
 
0.4%
7 36
 
0.4%
8 28
 
0.3%
9 28
 
0.3%

Spa
Real number (ℝ)

 Before imputationAfter imputation
Distinct13271441
Distinct (%)15.6%16.6%
Missing1830
Missing (%)2.1%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean311.13878311.25812
 Before imputationAfter imputation
Minimum0-67.375759
Maximum2240822408
Zeros53245393
Zeros (%)61.2%62.0%
Negative05
Negative (%)0.0%0.1%
Memory size68.0 KiB68.0 KiB
2024-04-23T20:26:24.016102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 Before imputationAfter imputation
Minimum0-67.375759
5-th percentile00
Q100
median00
Q35971
95-th percentile1607.11611.4
Maximum2240822408
Range2240822475.376
Interquartile range (IQR)5971

Descriptive statistics

 Before imputationAfter imputation
Standard deviation1136.70551127.664
Coefficient of variation (CV)3.65337153.6229224
Kurtosis81.2021182.114
Mean311.13878311.25812
Median Absolute Deviation (MAD)00
Skewness7.63601997.6630794
Sum26477912705766.9
Variance1292099.51271626.2
MonotonicityNot monotonicNot monotonic
2024-04-23T20:26:24.310462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5324
61.2%
1 146
 
1.7%
2 105
 
1.2%
3 53
 
0.6%
5 53
 
0.6%
4 46
 
0.5%
7 34
 
0.4%
6 33
 
0.4%
9 28
 
0.3%
8 28
 
0.3%
Other values (1317) 2660
30.6%
(Missing) 183
 
2.1%
ValueCountFrequency (%)
0 5393
62.0%
1 146
 
1.7%
2 105
 
1.2%
5 53
 
0.6%
3 53
 
0.6%
4 46
 
0.5%
7 34
 
0.4%
6 33
 
0.4%
8 28
 
0.3%
9 28
 
0.3%
Other values (1431) 2774
31.9%
ValueCountFrequency (%)
0 5324
61.2%
1 146
 
1.7%
2 105
 
1.2%
3 53
 
0.6%
4 46
 
0.5%
5 53
 
0.6%
6 33
 
0.4%
7 34
 
0.4%
8 28
 
0.3%
9 28
 
0.3%
ValueCountFrequency (%)
-67.3757589 1
 
< 0.1%
-10.48259565 1
 
< 0.1%
-6.989195279 1
 
< 0.1%
-6.506341041 1
 
< 0.1%
-0.184145847 1
 
< 0.1%
0 5393
62.0%
1 146
 
1.7%
2 105
 
1.2%
3 53
 
0.6%
4 46
 
0.5%
ValueCountFrequency (%)
-67.3757589 1
 
< 0.1%
-10.48259565 1
 
< 0.1%
-6.989195279 1
 
< 0.1%
-6.506341041 1
 
< 0.1%
-0.184145847 1
 
< 0.1%
0 5393
62.0%
1 146
 
1.7%
2 105
 
1.2%
3 53
 
0.6%
4 46
 
0.5%
ValueCountFrequency (%)
0 5324
61.2%
1 146
 
1.7%
2 105
 
1.2%
3 53
 
0.6%
4 46
 
0.5%
5 53
 
0.6%
6 33
 
0.4%
7 34
 
0.4%
8 28
 
0.3%
9 28
 
0.3%

VRDeck
Real number (ℝ)

 Before imputationAfter imputation
Distinct13061413
Distinct (%)15.4%16.3%
Missing1880
Missing (%)2.2%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean304.85479303.54529
 Before imputationAfter imputation
Minimum0-129.47032
Maximum2413324133
Zeros54955576
Zeros (%)63.2%64.1%
Negative09
Negative (%)0.0%0.1%
Memory size68.0 KiB68.0 KiB
2024-04-23T20:26:24.652706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 Before imputationAfter imputation
Minimum0-129.47032
5-th percentile00
Q100
median00
Q34652.407262
95-th percentile1534.21514
Maximum2413324133
Range2413324262.47
Interquartile range (IQR)4652.407262

Descriptive statistics

 Before imputationAfter imputation
Standard deviation1145.71721136.936
Coefficient of variation (CV)3.75823913.7455236
Kurtosis86.01118686.886046
Mean304.85479303.54529
Median Absolute Deviation (MAD)00
Skewness7.81973167.8470745
Sum25927902638719.2
Variance1312667.91292623.6
MonotonicityNot monotonicNot monotonic
2024-04-23T20:26:24.940985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5495
63.2%
1 139
 
1.6%
2 70
 
0.8%
3 56
 
0.6%
5 51
 
0.6%
4 47
 
0.5%
6 32
 
0.4%
8 30
 
0.3%
7 29
 
0.3%
9 25
 
0.3%
Other values (1296) 2531
29.1%
(Missing) 188
 
2.2%
ValueCountFrequency (%)
0 5576
64.1%
1 139
 
1.6%
2 70
 
0.8%
3 56
 
0.6%
5 51
 
0.6%
4 47
 
0.5%
6 32
 
0.4%
8 30
 
0.3%
7 29
 
0.3%
9 25
 
0.3%
Other values (1403) 2638
30.3%
ValueCountFrequency (%)
0 5495
63.2%
1 139
 
1.6%
2 70
 
0.8%
3 56
 
0.6%
4 47
 
0.5%
5 51
 
0.6%
6 32
 
0.4%
7 29
 
0.3%
8 30
 
0.3%
9 25
 
0.3%
ValueCountFrequency (%)
-129.4703202 1
 
< 0.1%
-123.0460894 1
 
< 0.1%
-102.4264273 1
 
< 0.1%
-72.22024974 1
 
< 0.1%
-71.60520892 1
 
< 0.1%
-37.93002098 1
 
< 0.1%
-29.98420841 1
 
< 0.1%
-23.63495899 1
 
< 0.1%
-6.684265716 1
 
< 0.1%
0 5576
64.1%
ValueCountFrequency (%)
-129.4703202 1
 
< 0.1%
-123.0460894 1
 
< 0.1%
-102.4264273 1
 
< 0.1%
-72.22024974 1
 
< 0.1%
-71.60520892 1
 
< 0.1%
-37.93002098 1
 
< 0.1%
-29.98420841 1
 
< 0.1%
-23.63495899 1
 
< 0.1%
-6.684265716 1
 
< 0.1%
0 5576
64.1%
ValueCountFrequency (%)
0 5495
63.2%
1 139
 
1.6%
2 70
 
0.8%
3 56
 
0.6%
4 47
 
0.5%
5 51
 
0.6%
6 32
 
0.4%
7 29
 
0.3%
8 30
 
0.3%
9 25
 
0.3%

Transported
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
1
4378 
0
4315 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 4378
50.4%
0 4315
49.6%

Length

2024-04-23T20:26:25.138574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 4378
50.4%
0 4315
49.6%

Most occurring characters

ValueCountFrequency (%)
1 4378
50.4%
0 4315
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4378
50.4%
0 4315
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4378
50.4%
0 4315
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4378
50.4%
0 4315
49.6%

Cabin_deck
Categorical

 Before imputationAfter imputation
Distinct88
Distinct (%)0.1%0.1%
Missing1990
Missing (%)2.3%0.0%
Memory size68.0 KiB68.0 KiB
F
2794 
G
2559 
E
876 
B
779 
C
747 
Other values (3)
739 
F
2868 
G
2615 
E
877 
B
819 
C
768 
Other values (3)
746 

Length

 Before imputationAfter imputation
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 Before imputationAfter imputation
Total characters84948693
Distinct characters88
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Before imputationAfter imputation
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Before imputationAfter imputation
1st rowBB
2nd rowFF
3rd rowAA
4th rowAA
5th rowFF

Common Values

ValueCountFrequency (%)
F 2794
32.1%
G 2559
29.4%
E 876
 
10.1%
B 779
 
9.0%
C 747
 
8.6%
D 478
 
5.5%
A 256
 
2.9%
T 5
 
0.1%
(Missing) 199
 
2.3%
ValueCountFrequency (%)
F 2868
33.0%
G 2615
30.1%
E 877
 
10.1%
B 819
 
9.4%
C 768
 
8.8%
D 483
 
5.6%
A 258
 
3.0%
T 5
 
0.1%

Length

2024-04-23T20:26:25.378209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Before imputation

2024-04-23T20:26:25.588404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:25.802562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
f 2794
32.9%
g 2559
30.1%
e 876
 
10.3%
b 779
 
9.2%
c 747
 
8.8%
d 478
 
5.6%
a 256
 
3.0%
t 5
 
0.1%
ValueCountFrequency (%)
f 2868
33.0%
g 2615
30.1%
e 877
 
10.1%
b 819
 
9.4%
c 768
 
8.8%
d 483
 
5.6%
a 258
 
3.0%
t 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
F 2794
32.9%
G 2559
30.1%
E 876
 
10.3%
B 779
 
9.2%
C 747
 
8.8%
D 478
 
5.6%
A 256
 
3.0%
T 5
 
0.1%
ValueCountFrequency (%)
F 2868
33.0%
G 2615
30.1%
E 877
 
10.1%
B 819
 
9.4%
C 768
 
8.8%
D 483
 
5.6%
A 258
 
3.0%
T 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8494
100.0%
ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 2794
32.9%
G 2559
30.1%
E 876
 
10.3%
B 779
 
9.2%
C 747
 
8.8%
D 478
 
5.6%
A 256
 
3.0%
T 5
 
0.1%
ValueCountFrequency (%)
F 2868
33.0%
G 2615
30.1%
E 877
 
10.1%
B 819
 
9.4%
C 768
 
8.8%
D 483
 
5.6%
A 258
 
3.0%
T 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8494
100.0%
ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 2794
32.9%
G 2559
30.1%
E 876
 
10.3%
B 779
 
9.2%
C 747
 
8.8%
D 478
 
5.6%
A 256
 
3.0%
T 5
 
0.1%
ValueCountFrequency (%)
F 2868
33.0%
G 2615
30.1%
E 877
 
10.1%
B 819
 
9.4%
C 768
 
8.8%
D 483
 
5.6%
A 258
 
3.0%
T 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8494
100.0%
ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 2794
32.9%
G 2559
30.1%
E 876
 
10.3%
B 779
 
9.2%
C 747
 
8.8%
D 478
 
5.6%
A 256
 
3.0%
T 5
 
0.1%
ValueCountFrequency (%)
F 2868
33.0%
G 2615
30.1%
E 877
 
10.1%
B 819
 
9.4%
C 768
 
8.8%
D 483
 
5.6%
A 258
 
3.0%
T 5
 
0.1%

Cabin_side
Categorical

 Before imputationAfter imputation
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing1990
Missing (%)2.3%0.0%
Memory size68.0 KiB68.0 KiB
S
4288 
P
4206 
S
4387 
P
4306 

Length

 Before imputationAfter imputation
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 Before imputationAfter imputation
Total characters84948693
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Before imputationAfter imputation
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Before imputationAfter imputation
1st rowPP
2nd rowSS
3rd rowSS
4th rowSS
5th rowSS

Common Values

ValueCountFrequency (%)
S 4288
49.3%
P 4206
48.4%
(Missing) 199
 
2.3%
ValueCountFrequency (%)
S 4387
50.5%
P 4306
49.5%

Length

2024-04-23T20:26:26.031161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Before imputation

2024-04-23T20:26:26.199878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:26.377439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
s 4288
50.5%
p 4206
49.5%
ValueCountFrequency (%)
s 4387
50.5%
p 4306
49.5%

Most occurring characters

ValueCountFrequency (%)
S 4288
50.5%
P 4206
49.5%
ValueCountFrequency (%)
S 4387
50.5%
P 4306
49.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8494
100.0%
ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4288
50.5%
P 4206
49.5%
ValueCountFrequency (%)
S 4387
50.5%
P 4306
49.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8494
100.0%
ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4288
50.5%
P 4206
49.5%
ValueCountFrequency (%)
S 4387
50.5%
P 4306
49.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8494
100.0%
ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4288
50.5%
P 4206
49.5%
ValueCountFrequency (%)
S 4387
50.5%
P 4306
49.5%

ID_group
Real number (ℝ)

Distinct6217
Distinct (%)71.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4633.3896
Minimum1
Maximum9280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-04-23T20:26:26.616318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile465.6
Q12319
median4630
Q36883
95-th percentile8819.4
Maximum9280
Range9279
Interquartile range (IQR)4564

Descriptive statistics

Standard deviation2671.0289
Coefficient of variation (CV)0.57647404
Kurtosis-1.1817463
Mean4633.3896
Median Absolute Deviation (MAD)2277
Skewness0.0020202219
Sum40278056
Variance7134395.1
MonotonicityIncreasing
2024-04-23T20:26:26.824792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4498 8
 
0.1%
8168 8
 
0.1%
8728 8
 
0.1%
8796 8
 
0.1%
8956 8
 
0.1%
4256 8
 
0.1%
984 8
 
0.1%
9081 8
 
0.1%
8988 8
 
0.1%
5756 8
 
0.1%
Other values (6207) 8613
99.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
3 2
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 2
< 0.1%
7 1
 
< 0.1%
8 3
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
9280 2
< 0.1%
9279 1
 
< 0.1%
9278 1
 
< 0.1%
9276 1
 
< 0.1%
9275 3
< 0.1%
9274 1
 
< 0.1%
9272 2
< 0.1%
9270 1
 
< 0.1%
9268 1
 
< 0.1%
9267 2
< 0.1%

ID_num
Real number (ℝ)

 Before imputationAfter imputation
Distinct88
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.51777291.5177729
 Before imputationAfter imputation
Minimum11
Maximum88
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size68.0 KiB68.0 KiB
2024-04-23T20:26:27.008736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 Before imputationAfter imputation
Minimum11
5-th percentile11
Q111
median11
Q322
95-th percentile44
Maximum88
Range77
Interquartile range (IQR)11

Descriptive statistics

 Before imputationAfter imputation
Standard deviation1.05424131.0542413
Coefficient of variation (CV)0.694597530.69459753
Kurtosis8.70926288.7092628
Mean1.51777291.5177729
Median Absolute Deviation (MAD)00
Skewness2.74661682.7466168
Sum1319413194
Variance1.11142481.1114248
MonotonicityNot monotonicNot monotonic
2024-04-23T20:26:27.206633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 6217
71.5%
2 1412
 
16.2%
3 571
 
6.6%
4 231
 
2.7%
5 128
 
1.5%
6 75
 
0.9%
7 46
 
0.5%
8 13
 
0.1%
ValueCountFrequency (%)
1 6217
71.5%
2 1412
 
16.2%
3 571
 
6.6%
4 231
 
2.7%
5 128
 
1.5%
6 75
 
0.9%
7 46
 
0.5%
8 13
 
0.1%
ValueCountFrequency (%)
1 6217
71.5%
2 1412
 
16.2%
3 571
 
6.6%
4 231
 
2.7%
5 128
 
1.5%
6 75
 
0.9%
7 46
 
0.5%
8 13
 
0.1%
ValueCountFrequency (%)
1 6217
71.5%
2 1412
 
16.2%
3 571
 
6.6%
4 231
 
2.7%
5 128
 
1.5%
6 75
 
0.9%
7 46
 
0.5%
8 13
 
0.1%
ValueCountFrequency (%)
1 6217
71.5%
2 1412
 
16.2%
3 571
 
6.6%
4 231
 
2.7%
5 128
 
1.5%
6 75
 
0.9%
7 46
 
0.5%
8 13
 
0.1%
ValueCountFrequency (%)
1 6217
71.5%
2 1412
 
16.2%
3 571
 
6.6%
4 231
 
2.7%
5 128
 
1.5%
6 75
 
0.9%
7 46
 
0.5%
8 13
 
0.1%

Group_size
Real number (ℝ)

 Before imputationAfter imputation
Distinct88
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.03554582.0355458
 Before imputationAfter imputation
Minimum11
Maximum88
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size68.0 KiB68.0 KiB
2024-04-23T20:26:27.404482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 Before imputationAfter imputation
Minimum11
5-th percentile11
Q111
median11
Q333
95-th percentile66
Maximum88
Range77
Interquartile range (IQR)22

Descriptive statistics

 Before imputationAfter imputation
Standard deviation1.59634651.5963465
Coefficient of variation (CV)0.784235110.78423511
Kurtosis3.16709583.1670958
Mean2.03554582.0355458
Median Absolute Deviation (MAD)00
Skewness1.88901731.8890173
Sum1769517695
Variance2.54832222.5483222
MonotonicityNot monotonicNot monotonic
2024-04-23T20:26:27.608103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 4805
55.3%
2 1682
 
19.3%
3 1020
 
11.7%
4 412
 
4.7%
5 265
 
3.0%
7 231
 
2.7%
6 174
 
2.0%
8 104
 
1.2%
ValueCountFrequency (%)
1 4805
55.3%
2 1682
 
19.3%
3 1020
 
11.7%
4 412
 
4.7%
5 265
 
3.0%
7 231
 
2.7%
6 174
 
2.0%
8 104
 
1.2%
ValueCountFrequency (%)
1 4805
55.3%
2 1682
 
19.3%
3 1020
 
11.7%
4 412
 
4.7%
5 265
 
3.0%
6 174
 
2.0%
7 231
 
2.7%
8 104
 
1.2%
ValueCountFrequency (%)
1 4805
55.3%
2 1682
 
19.3%
3 1020
 
11.7%
4 412
 
4.7%
5 265
 
3.0%
6 174
 
2.0%
7 231
 
2.7%
8 104
 
1.2%
ValueCountFrequency (%)
1 4805
55.3%
2 1682
 
19.3%
3 1020
 
11.7%
4 412
 
4.7%
5 265
 
3.0%
6 174
 
2.0%
7 231
 
2.7%
8 104
 
1.2%
ValueCountFrequency (%)
1 4805
55.3%
2 1682
 
19.3%
3 1020
 
11.7%
4 412
 
4.7%
5 265
 
3.0%
6 174
 
2.0%
7 231
 
2.7%
8 104
 
1.2%

Interactions

Before imputation

2024-04-23T20:26:03.684029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:16.894810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:50.843913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:07.111452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:52.495739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:08.561667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:54.295509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:09.950154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:55.934883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:11.383839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:57.716870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:12.663306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:59.166351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:13.967205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:00.683528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:26:02.170637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:15.443411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:03.867007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:17.045110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:51.091379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:07.338388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:52.678997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:08.756141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:54.488393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:10.131004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:56.094098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:11.552056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:57.879663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:12.840391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:59.351285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:14.173339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:00.838774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:26:02.327672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:15.642121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:04.063780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:17.222066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:51.279902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:07.517501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:52.848162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:08.933206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:54.671057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:10.325195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:56.268724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:11.703768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:58.040379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:13.022591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:59.541451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:14.360678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:01.021257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:26:02.491706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:15.797990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:04.235843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:17.403555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:51.451586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:07.682406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:53.059508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:09.108945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:54.854166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:10.502187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:56.427610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:11.880999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:58.249650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:13.179071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:59.715335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:14.581957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:01.177444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:26:02.630679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:15.989541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:04.398953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:17.607658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:51.640228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:07.860749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:53.215892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:09.269590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:55.017025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:10.704162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:56.635353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:12.035719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:58.414551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:13.339230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:59.847465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:14.719818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:01.329174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:26:02.822852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:16.132365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:04.589784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:17.758011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:51.824936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:08.001335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:53.376370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:09.437218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:55.205002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:10.875474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:56.805119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:12.172271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:58.560318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:13.470248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:00.005484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:14.879287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:01.474007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:26:02.968272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:16.310958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:04.751315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:25:51.995807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:25:53.610770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:25:55.402101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:25:56.950400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:25:58.709071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:26:00.165499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:26:01.654885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:26:03.117706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:26:04.915775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:18.196985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:52.133872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:08.150293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:53.896619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:09.601673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:55.552830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:11.039453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:57.131348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:12.322762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:58.858099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:13.639690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:00.313617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:15.044152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:01.818077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:26:03.274653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:16.534767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:05.099287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:18.359582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:52.291279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:08.356845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:54.115608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:09.780866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:55.736844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:11.203040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:57.543581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:12.492949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:25:59.008055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:13.812613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:00.514944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:15.236133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Before imputation

2024-04-23T20:26:02.013153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation


Interaction plot not present for dataset

Before imputation

2024-04-23T20:26:03.470587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

After imputation

2024-04-23T20:26:16.737067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

Before imputation

2024-04-23T20:26:05.603810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.

After imputation

2024-04-23T20:26:18.682784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.

Before imputation

2024-04-23T20:26:06.080784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

After imputation

2024-04-23T20:26:19.044784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Before imputation

HomePlanetCryoSleepDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckTransportedCabin_deckCabin_sideID_groupID_numGroup_size
0EuropaFalseTRAPPIST-1e39.0False0.00.00.00.00.00BP111
1EarthFalseTRAPPIST-1e24.0False109.09.025.0549.044.01FS211
2EuropaFalseTRAPPIST-1e58.0True43.03576.00.06715.049.00AS312
3EuropaFalseTRAPPIST-1e33.0False0.01283.0371.03329.0193.00AS322
4EarthFalseTRAPPIST-1e16.0False303.070.0151.0565.02.01FS411
5EarthFalsePSO J318.5-2244.0False0.0483.00.0291.00.01FP511
6EarthFalseTRAPPIST-1e26.0False42.01539.03.00.00.01FS612
7EarthTrueTRAPPIST-1e28.0False0.00.00.00.0NaN1GS622
8EarthFalseTRAPPIST-1e35.0False0.0785.017.0216.00.01FS711
9EuropaTrue55 Cancri e14.0False0.00.00.00.00.01BP813

After imputation

HomePlanetCryoSleepDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckCabin_sideID_numGroup_size
0EuropaFalseTRAPPIST-1e39.0False0.00.00.00.00.0BP11
1EarthFalseTRAPPIST-1e24.0False109.09.025.0549.044.0FS11
2EuropaFalseTRAPPIST-1e58.0True43.03576.00.06715.049.0AS12
3EuropaFalseTRAPPIST-1e33.0False0.01283.0371.03329.0193.0AS22
4EarthFalseTRAPPIST-1e16.0False303.070.0151.0565.02.0FS11
5EarthFalsePSO J318.5-2244.0False0.0483.00.0291.00.0FP11
6EarthFalseTRAPPIST-1e26.0False42.01539.03.00.00.0FS12
7EarthTrueTRAPPIST-1e28.0False0.00.00.00.00.0GS22
8EarthFalseTRAPPIST-1e35.0False0.0785.017.0216.00.0FS11
9EuropaTrue55 Cancri e14.0False0.00.00.00.00.0BP13

Before imputation

HomePlanetCryoSleepDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckTransportedCabin_deckCabin_sideID_groupID_numGroup_size
8683EarthFalseTRAPPIST-1e21.0False86.03.0149.0208.0329.00FP927222
8684NaNTrueTRAPPIST-1e23.0False0.00.00.00.00.01GP927411
8685EuropaFalseTRAPPIST-1e0.0False0.00.00.00.00.01AP927513
8686EuropaFalseTRAPPIST-1e32.0False1.01146.00.050.034.00AP927523
8687EuropaNaNTRAPPIST-1e30.0False0.03208.00.02.0330.01AP927533
8688EuropaFalse55 Cancri e41.0True0.06819.00.01643.074.00AP927611
8689EarthTruePSO J318.5-2218.0False0.00.00.00.00.00GS927811
8690EarthFalseTRAPPIST-1e26.0False0.00.01872.01.00.01GS927911
8691EuropaFalse55 Cancri e32.0False0.01049.00.0353.03235.00ES928012
8692EuropaFalseTRAPPIST-1e44.0False126.04688.00.00.012.01ES928022

After imputation

HomePlanetCryoSleepDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckCabin_sideID_numGroup_size
8683EarthFalseTRAPPIST-1e21.0False86.03.0149.0208.0329.0FP22
8684EarthTrueTRAPPIST-1e23.0False0.00.00.00.00.0GP11
8685EuropaFalseTRAPPIST-1e0.0False0.00.00.00.00.0AP13
8686EuropaFalseTRAPPIST-1e32.0False1.01146.00.050.034.0AP23
8687EuropaFalseTRAPPIST-1e30.0False0.03208.00.02.0330.0AP33
8688EuropaFalse55 Cancri e41.0True0.06819.00.01643.074.0AP11
8689EarthTruePSO J318.5-2218.0False0.00.00.00.00.0GS11
8690EarthFalseTRAPPIST-1e26.0False0.00.01872.01.00.0GS11
8691EuropaFalse55 Cancri e32.0False0.01049.00.0353.03235.0ES12
8692EuropaFalseTRAPPIST-1e44.0False126.04688.00.00.012.0ES22

Duplicate rows

Before imputation

HomePlanetCryoSleepDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckTransportedCabin_deckCabin_sideID_groupID_numGroup_size# duplicates
Dataset does not contain duplicate rows.

After imputation

HomePlanetCryoSleepDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckCabin_sideID_numGroup_size# duplicates
169EarthTrueTRAPPIST-1e14.0False0.00.00.00.00.0GS1113
181EarthTrueTRAPPIST-1e18.0False0.00.00.00.00.0GS1113
193EarthTrueTRAPPIST-1e22.0False0.00.00.00.00.0GP1113
85EarthTruePSO J318.5-2216.0False0.00.00.00.00.0GS1112
170EarthTrueTRAPPIST-1e15.0False0.00.00.00.00.0GP1112
180EarthTrueTRAPPIST-1e18.0False0.00.00.00.00.0GP1112
184EarthTrueTRAPPIST-1e19.0False0.00.00.00.00.0GP1112
195EarthTrueTRAPPIST-1e22.0False0.00.00.00.00.0GS1112
98EarthTruePSO J318.5-2222.0False0.00.00.00.00.0GP1111
172EarthTrueTRAPPIST-1e15.0False0.00.00.00.00.0GS1111